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Statistical Inference and Decision Lab
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A mathematical statistics lab working on statistical inference for stochastic process models.
AboutAbout the lab
Inferring stochastic process models from data.
The Statistical Inference and Decision Group (Uchida Lab) conducts research and education on statistical inference for stochastic process models.
Combining mathematical statistics with computer-based theoretical statistics, the lab works to elucidate phenomena described by stochastic process models based on data.
Statistical inference for stochastic process models is applied to financial data analysis, actuarial statistics for insurance, statistical seismology, and more.
The lab belongs to the Mathematical and Quantitative Finance Chair in the Social Systems Mathematics Area of the Department of Systems Innovation, Graduate School of Engineering Science.
Key terms
- stochastic process
- Random variation that unfolds over time. Data are considered to be generated by it, and estimation and prediction are carried out
- measure theory
- A field of mathematics that underlies modern probability theory and mathematical statistics
- diffusion process
- A type of complex stochastic process used in mathematical finance models
NewsLab news
A paper by Professor Uchida and colleagues was published in Japanese Journal of Statistics and Data Science
A paper by Lecturer Takabatake and colleagues was published in Annals of the Institute of Statistical Mathematics
A master's student received the Graduate School of Engineering Science Award
An undergraduate student received the School of Engineering Science Award
A paper by Professor Uchida and colleagues was published in Journal of Statistical Planning and Inference
ResearchResearch themes
Centered on statistical inference for stochastic process models, the lab combines mathematical statistics with computer-based theoretical statistics to elucidate phenomena from data. People interested not only in theoretical statistics but also in financial and actuarial statistics and statistical seismology are welcome.
*Illustrative imageFoundations of probability and statistics
Modern probability theory and mathematical statistics are all built on measure theory. Learning these foundations is recommended for handling complex probability and statistical models.
*Illustrative imageStochastic processes and data analysis
A stochastic process is random variation that unfolds over time. Treating data on many social phenomena as generated by some kind of stochastic process, the lab carries out estimation and prediction.
*Illustrative imageMathematical statistics for finance and insurance
Mathematical finance models use complex stochastic process models such as diffusion processes and Lévy processes. Statistical analysis is considered essential to make these meaningful in practice.
MembersFaculty
The members of the Statistical Inference and Decision Group (Uchida Lab) are faculty and master's students.As of 2026
- ProfessorMasayuki Uchida
- LecturerTetsuya Takabatake
VoicesStudent voices
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Report an errorAbout the images
The top and research theme images are illustrative and were not taken at the lab. They are cropped from Wikimedia Commons images. Cropped CC BY-SA images are provided under the same CC BY-SA license as the original (version shown on each line).
- Top image: “Csm Brownian-Motion f99de6516a” NivedRajeev / CC BY-SA 4.0 (cropped)
- Foundations of probability and statistics: “Coin Toss ( )” ICMA Photos / CC BY-SA 2.0 (cropped)
- Stochastic processes and data analysis: “BMonSphere” Thomas Steiner / CC BY-SA 2.5 (cropped)
- Mathematical statistics for finance and insurance: “New York Stock Exchange August 2017 02” Arild Vågen / CC BY-SA 4.0 (cropped)
- Related lab (Differential Equations Lab): “Controur Boxplots Fluid Simulation median, order statistics, and outliers” Sciinstitute / CC BY-SA 3.0 (cropped)
- Related lab (Applied Analysis Lab): “PCBThermalSimulation” Harke / Public domain (cropped)
- Related lab (Data Science Lab): “Antique one-armed bandit, Ventnor, Isle of Wight, UK” Ronald Saunders from Warrington, UK / CC BY-SA 4.0 (cropped)
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